{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1000行数据\n",
    "\n",
    "每样本对应三个属性：\n",
    "每年获得的飞行常客里程数\n",
    "玩视频游戏所耗时间百分比\n",
    "每周消费的冰琪淋公升数\n",
    "\n",
    "类别：\n",
    "不喜欢的人     1\n",
    "魅力一般的人   2\n",
    "极具魅力的人   3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 3)\n",
      "<type 'numpy.ndarray'>\n",
      "[[  4.09200000e+04   8.32697600e+00   9.53952000e-01]\n",
      " [  1.44880000e+04   7.15346900e+00   1.67390400e+00]\n",
      " [  2.60520000e+04   1.44187100e+00   8.05124000e-01]\n",
      " ..., \n",
      " [  2.65750000e+04   1.06501020e+01   8.66627000e-01]\n",
      " [  4.81110000e+04   9.13452800e+00   7.28045000e-01]\n",
      " [  4.37570000e+04   7.88260100e+00   1.33244600e+00]]\n",
      "[3, 2, 1, 1, 1, 1, 3, 3, 1, 3, 1, 1, 2, 1, 1, 1, 1, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# load data\n",
    "def file2matrix(filename):\n",
    "    fr = open(filename)\n",
    "    arrayOlines = fr.readlines()\n",
    "    numberOfLines = len(arrayOlines) #❶ 得到文件行数\n",
    "    returnMat = np.zeros((numberOfLines,3)) #❷ 创建返回的Numpy矩阵\n",
    "    classLabelVector = []\n",
    "    index = 0\n",
    "    #❸ （以下三行） 解析文件数据到列表\n",
    "    for line in arrayOlines:\n",
    "        line = line.strip()\n",
    "        listFromLine = line.split('\\t')\n",
    "        returnMat[index,:] = listFromLine[0:3]\n",
    "        classLabelVector.append(int(listFromLine[-1]))\n",
    "        index += 1\n",
    "    return returnMat,classLabelVector\n",
    "\n",
    "datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')\n",
    "\n",
    "print(datingDataMat.shape)\n",
    "print(type(datingDataMat))\n",
    "print(datingDataMat)\n",
    "print(datingLabels[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4107b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 散点图\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(211)\n",
    "ax.scatter(datingDataMat[:,1], datingDataMat[:,2])\n",
    "ax.set_title(\"Scatter\")\n",
    "ax.set_xlabel(\"the percent of time for game\")\n",
    "ax.set_ylabel(\"ice cream spend per week\")\n",
    "\n",
    "# 带样本分类的标记图\n",
    "ax2 = fig.add_subplot(212)\n",
    "ax2.scatter(datingDataMat[:,1], datingDataMat[:,2],\n",
    "           15.0*np.array(datingLabels), 15.0*np.array(datingLabels))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.44832535  0.39805139  0.56233353]\n",
      " [ 0.15873259  0.34195467  0.98724416]\n",
      " [ 0.28542943  0.06892523  0.47449629]\n",
      " ..., \n",
      " [ 0.29115949  0.50910294  0.51079493]\n",
      " [ 0.52711097  0.43665451  0.4290048 ]\n",
      " [ 0.47940793  0.3768091   0.78571804]]\n",
      "[  9.12730000e+04   2.09193490e+01   1.69436100e+00]\n",
      "[ 0.        0.        0.001156]\n"
     ]
    }
   ],
   "source": [
    "# 归一化特征值\n",
    "def autoNorm(dataSet):\n",
    "    '''\n",
    "    每列的最小值放在变量minVals中， \n",
    "    将最大值放在变量maxVals中， \n",
    "    其中dataSet.min(0)中的参数0使得函数可以从列中选取最小值， 而不是选取当前行的最小值。\n",
    "    '''\n",
    "    minVals = dataSet.min(0)\n",
    "    maxVals = dataSet.max(0)\n",
    "    ranges = maxVals - minVals\n",
    "    normDataSet = np.zeros(np.shape(dataSet))\n",
    "    m = dataSet.shape[0]\n",
    "    normDataSet = dataSet - np.tile(minVals, (m,1))\n",
    "    normDataSet = normDataSet/np.tile(ranges, (m,1)) #❶ 特征值相除\n",
    "    return normDataSet, ranges, minVals\n",
    "\n",
    "normMat, ranges, minVals = autoNorm(datingDataMat)\n",
    "print(normMat)\n",
    "print(ranges)\n",
    "print(minVals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the total error rate is: 0.064000\n",
      "32.0\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "import operator\n",
    "\n",
    "def classify0(inX, dataSet, labels, k):\n",
    "    dataSetSize = dataSet.shape[0]\n",
    "    #❶（以下三行） 距离计算\n",
    "    # np.tile(a,(2,1))就是把a先沿x轴（就这样称呼吧）复制1倍，即没有复制，仍然是 [0,1,2],再把结果沿y方向复制2倍\n",
    "    diffMat = np.tile(inX, (dataSetSize,1)) - dataSet  #np.tile把数组沿各个方向复制\n",
    "    sqDiffMat = diffMat**2\n",
    "    sqDistances = sqDiffMat.sum(axis=1)\n",
    "    distances = sqDistances**0.5\n",
    "    sortedDistIndicies = distances.argsort()\n",
    "    classCount={}\n",
    "    #❷ （以下两行） 选择距离最小的k个点,统计频率\n",
    "    for i in range(k):\n",
    "        voteIlabel = labels[sortedDistIndicies[i]]\n",
    "        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1\n",
    "    #❸ 排序，字典根据词频降序\n",
    "    sortedClassCount = sorted(classCount.iteritems(),key=operator.itemgetter(1), reverse=True)        \n",
    "    return sortedClassCount[0][0]\n",
    "\n",
    "\n",
    "\n",
    "# 完整训练\n",
    "def datingClassTest():\n",
    "    hoRatio = 0.50      #hold out 10%\n",
    "    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')       #load data setfrom file\n",
    "    normMat, ranges, minVals = autoNorm(datingDataMat)\n",
    "    m = normMat.shape[0]\n",
    "    numTestVecs = int(m*hoRatio)\n",
    "    errorCount = 0.0\n",
    "    for i in range(numTestVecs):\n",
    "        classifierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],datingLabels[numTestVecs:m],3)\n",
    "        #print \"the classifier came back with: %d, the real answer is: %d\" % (classifierResult, datingLabels[i])\n",
    "        if (classifierResult != datingLabels[i]): errorCount += 1.0\n",
    "    print \"the total error rate is: %f\" % (errorCount/float(numTestVecs))\n",
    "    print errorCount\n",
    "    \n",
    "print(datingClassTest())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You will probably like this person:  in large doses\n"
     ]
    }
   ],
   "source": [
    "# 构建完整可用系统\n",
    "def classifyPerson():\n",
    "    resultList = ['not at all','in small doses', 'in large doses']\n",
    "    percentTats = float(raw_input(\"percentage of time spent playing video games?\"))\n",
    "    ffMiles = float(raw_input(\"frequent flier miles earned per year?\"))\n",
    "    iceCream = float(raw_input(\"liters of ice cream consumed per year?\"))\n",
    "    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')\n",
    "    normMat, ranges, minVals = autoNorm(datingDataMat)\n",
    "    inArr = np.array([ffMiles, percentTats, iceCream])\n",
    "    classifierResult = classify0((inArr-minVals)/ranges,normMat,datingLabels,3)\n",
    "    print \"You will probably like this person: \", resultList[classifierResult - 1]\n",
    "    \n",
    "classifyPerson()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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